A low-light image enhancement method based on HSV space

Abstract

ABSTRACT To enhance the visual performance of low-illumination images, many low-illumination images are analyzed. Based on this, a low-light image enhancement method based on HSV space and semi-implicit ROF model is proposed. First, the low-illumination image is decomposed into HSV space for saturation denoising and brightness enhancement. Then, the Bayesian rules are applied to fuse the saturation and value. The three components in HSV space are converted to the RGB space and obtain a rough enhanced image. Finally, the semi-implicit ROF model is introduced to denoise the global noise and obtain the enhanced image. Such a comprehensive method can improve the low illumination image more clearly. The experimental results show that the algorithm has a PSNR score of 26.48, 6.29, 0.8947, and 28.4124, and the PSNR score is the highest in the comparison algorithm. The experiments on the Low-Light image data set also show that the proposed method can effectively improve the visibility of low-light images, and can provide a simple and effective method for low-light image enhancement.